Parallel beamlet dose calculation via beamlet contexts in a distributed multi‐GPU framework

• DOI: 10.1002/mp.13651
• Publication Date: 2019-06-11T03:08:08Z
• Authors: Ryan Neph; Cheng Ouyang; John Neylon; Youming Yang; Ke Sheng
• Keywords: Radiotherapy Planning; Oncology and Carcinogenesis; GPU; Biomedical Engineering; Bioengineering; dose calculation; Radiation Dosage; Phantoms; Imaging; distributed computing; 03 medical and health sciences; Computer-Assisted; 0302 clinical medicine; Intensity-Modulated; Computer Graphics; convolution; Radiometry; Medical and biological physics; Radiotherapy; Phantoms, Imaging; Radiotherapy Planning, Computer-Assisted; Radiotherapy Dosage; Medical and Biological Physics; Other Physical Sciences; Nuclear Medicine & Medical Imaging; Physical Sciences; Radiotherapy, Intensity-Modulated; beamlet; Biomedical engineering; Monte Carlo Method

PurposeDose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model‐based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under‐utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi‐GPU computational nodes.MethodsThe proposed context‐based transformation separates beamlet‐local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed‐cone convolution superposition procedure, producing the set of beamlet‐specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager‐worker architecture is constructed around the context‐based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context‐based approach compared to Monte Carlo and a reference CPU‐CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet‐sequential GPU‐CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.ResultsThe dosimetric accuracy of the context‐based method displays <1.35% and 2.35% average error from the existing serialized CPU‐CCCS algorithm and Monte Carlo simulation for beamlet‐specific PDDs in water and slab phantoms, respectively. The context‐based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet‐sequential GPU‐CCCS method in the tested configurations. The context‐based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.ConclusionsThe context‐based approach demonstrates a new expectation of performance for beamlet‐based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large‐scale treatment planning problems ‐ such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets ‐ in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large‐scale treatment planning tasks and clinical workflows.